






from __future__ import annotations

from typing import Any


class 							__UpperCamelCase     :







	def __init__(	self   :				Optional[Any]							,					_lowerCAmelCase   :				int							,					_lowerCAmelCase   :				int							,					_lowerCAmelCase   :				float = 0      )		->			None:
		"""simple docstring"""







		__lowercase							,    __lowercase   							=	row, column
		__lowercase   							=	[[default_value for c in range(_lowerCAmelCase      )] for r in range(_lowerCAmelCase      )]







	def __str__(	self   :				List[Any]      )		->			str:
		"""simple docstring"""







		__lowercase   							=	F'Matrix consist of {self.row} rows and {self.column} columns\n'

		# Make string identifier
		__lowercase   							=	0
		for row_vector in self.array:
			for obj in row_vector:
				__lowercase   							=	max(_lowerCAmelCase							,					len(str(_lowerCAmelCase      )      )      )
		__lowercase   							=	F'%{max_element_length}s'

		# Make string and return
		def single_line(_lowerCAmelCase   :				list[float]      ) -> str:
			nonlocal string_format_identifier
			__lowercase   							=	"""["""
			line += ", ".join(string_format_identifier % (obj,) for obj in row_vector      )
			line += "]"
			return line

		s += "\n".join(single_line(_lowerCAmelCase      ) for row_vector in self.array      )
		return s







	def __repr__(	self   :				int      )		->			str:
		"""simple docstring"""







		return str(self      )







	def 			_a (	self   :				Dict							,					_lowerCAmelCase   :				tuple[int, int]      )		->			bool:
		"""simple docstring"""







		if not (isinstance(_lowerCAmelCase							,					(list, tuple)      ) and len(_lowerCAmelCase      ) == 2):
			return False
		elif not (0 <= loc[0] < self.row and 0 <= loc[1] < self.column):
			return False
		else:
			return True







	def __getitem__(	self   :				List[str]							,					_lowerCAmelCase   :				tuple[int, int]      )		->			Any:
		"""simple docstring"""







		assert self.validate_indicies(_lowerCAmelCase      )
		return self.array[loc[0]][loc[1]]







	def __setitem__(	self   :				List[Any]							,					_lowerCAmelCase   :				tuple[int, int]							,					_lowerCAmelCase   :				float      )		->			None:
		"""simple docstring"""







		assert self.validate_indicies(_lowerCAmelCase      )
		__lowercase   							=	value







	def __add__(	self   :				Any							,					_lowerCAmelCase   :				Matrix      )		->			Matrix:
		"""simple docstring"""







		assert isinstance(_lowerCAmelCase							,					_lowerCAmelCase      )
		assert self.row == another.row and self.column == another.column

		# Add
		__lowercase   							=	Matrix(self.row							,					self.column      )
		for r in range(self.row      ):
			for c in range(self.column      ):
				__lowercase   							=	self[r, c] + another[r, c]
		return result







	def __neg__(	self   :				str      )		->			Matrix:
		"""simple docstring"""







		__lowercase   							=	Matrix(self.row							,					self.column      )
		for r in range(self.row      ):
			for c in range(self.column      ):
				__lowercase   							=	-self[r, c]
		return result







	def __sub__(	self   :				Tuple							,					_lowerCAmelCase   :				Matrix      )		->			Matrix:
		"""simple docstring"""







		return self + (-another)







	def __mul__(	self   :				Dict							,					_lowerCAmelCase   :				int | float | Matrix      )		->			Matrix:
		"""simple docstring"""







		if isinstance(_lowerCAmelCase							,					(int, float)      ):  # Scalar multiplication
			__lowercase   							=	Matrix(self.row							,					self.column      )
			for r in range(self.row      ):
				for c in range(self.column      ):
					__lowercase   							=	self[r, c] * another
			return result
		elif isinstance(_lowerCAmelCase							,					_lowerCAmelCase      ):  # Matrix multiplication
			assert self.column == another.row
			__lowercase   							=	Matrix(self.row							,					another.column      )
			for r in range(self.row      ):
				for c in range(another.column      ):
					for i in range(self.column      ):
						result[r, c] += self[r, i] * another[i, c]
			return result
		else:
			__lowercase   							=	F'Unsupported type given for another ({type(_lowerCAmelCase      )})'
			raise TypeError(_lowerCAmelCase      )







	def 			_a (	self   :				Any      )		->			Matrix:
		"""simple docstring"""







		__lowercase   							=	Matrix(self.column							,					self.row      )
		for r in range(self.row      ):
			for c in range(self.column      ):
				__lowercase   							=	self[r, c]
		return result







	def 			_a (	self   :				Dict							,					_lowerCAmelCase   :				Matrix							,					_lowerCAmelCase   :				Matrix      )		->			Any:
		"""simple docstring"""







		assert isinstance(_lowerCAmelCase							,					_lowerCAmelCase      ) and isinstance(_lowerCAmelCase							,					_lowerCAmelCase      )
		assert self.row == self.column == u.row == v.row  # u, v should be column vector
		assert u.column == v.column == 1  # u, v should be column vector

		# Calculate
		__lowercase   							=	v.transpose()
		__lowercase   							=	(v_t * self * u)[0, 0] + 1
		if numerator_factor == 0:
			return None  # It's not invertable
		return self - ((self * u) * (v_t * self) * (1.0 / numerator_factor))


# Testing
if __name__ == "__main__":





						def 					snake_case	(  ):




							'''simple docstring'''







							__lowercase   							=	Matrix(3					,	3					,	0       )
							for i in range(3       ):
								__lowercase   							=	1
							print(F'a^(-1) is {ainv}'       )
							# u, v
							__lowercase   							=	Matrix(3					,	1					,	0       )
							__lowercase							,    __lowercase							,    __lowercase   							=	1, 2, -3
							__lowercase   							=	Matrix(3					,	1					,	0       )
							__lowercase							,    __lowercase							,    __lowercase   							=	4, -2, 5
							print(F'u is {u}'       )
							print(F'v is {v}'       )
							print(F'uv^T is {u * v.transpose()}'       )
							# Sherman Morrison
							print(F'(a + uv^T)^(-1) is {ainv.sherman_morrison(lowerCamelCase					,	lowerCamelCase       )}'       )





						def 					snake_case	(  ):




							'''simple docstring'''







							import doctest

							doctest.testmod()




						testa()









"""simple docstring"""





import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyImgaImgPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()






class 	a		(   __snake_case    , unittest.TestCase						):
    SCREAMING_SNAKE_CASE		:	Optional[Any]		=					KandinskyImgaImgPipeline
    SCREAMING_SNAKE_CASE		:	Tuple		=					["""prompt""", """image_embeds""", """negative_image_embeds""", """image"""]
    SCREAMING_SNAKE_CASE		:	int		=					[
        """prompt""",
        """negative_prompt""",
        """image_embeds""",
        """negative_image_embeds""",
        """image""",
    ]
    SCREAMING_SNAKE_CASE		:	List[str]		=					[
        """generator""",
        """height""",
        """width""",
        """strength""",
        """guidance_scale""",
        """negative_prompt""",
        """num_inference_steps""",
        """return_dict""",
        """guidance_scale""",
        """num_images_per_prompt""",
        """output_type""",
        """return_dict""",
    ]
    SCREAMING_SNAKE_CASE		:	Any		=					False






    @property
    def   UpperCamelCase			(			self	:				List[str]   )	->						str:
      return 32






    @property
    def   UpperCamelCase			(			self	:				Tuple   )	->						Optional[Any]:
      return 32






    @property
    def   UpperCamelCase			(			self	:				Optional[int]   )	->						str:
      return self.time_input_dim






    @property
    def   UpperCamelCase			(			self	:				Optional[int]   )	->						Optional[Any]:
      return self.time_input_dim * 4






    @property
    def   UpperCamelCase			(			self	:				int   )	->						List[Any]:
      return 100






    @property
    def   UpperCamelCase			(			self	:				Any   )	->						List[Any]:
      lowerCamelCase_       	=		XLMRobertaTokenizerFast.from_pretrained('YiYiXu/tiny-random-mclip-base'   )
      return tokenizer






    @property
    def   UpperCamelCase			(			self	:				str   )	->						Optional[Any]:
      torch.manual_seed(0   )
      lowerCamelCase_       	=		MCLIPConfig(
          numDims=self.cross_attention_dim			,		transformerDimensions=self.text_embedder_hidden_size			,		hidden_size=self.text_embedder_hidden_size			,		intermediate_size=37			,		num_attention_heads=4			,		num_hidden_layers=5			,		vocab_size=1005			,		)

      lowerCamelCase_       	=		MultilingualCLIP(__SCREAMING_SNAKE_CASE   )
      lowerCamelCase_       	=		text_encoder.eval()

      return text_encoder






    @property
    def   UpperCamelCase			(			self	:				int   )	->						List[Any]:
      torch.manual_seed(0   )

      lowerCamelCase_       	=		{
          'in_channels': 4,
          # Out channels is double in channels because predicts mean and variance
          'out_channels': 8,
          'addition_embed_type': 'text_image',
          'down_block_types': ('ResnetDownsampleBlock2D', 'SimpleCrossAttnDownBlock2D'),
          'up_block_types': ('SimpleCrossAttnUpBlock2D', 'ResnetUpsampleBlock2D'),
          'mid_block_type': 'UNetMidBlock2DSimpleCrossAttn',
          'block_out_channels': (self.block_out_channels_a, self.block_out_channels_a * 2),
          'layers_per_block': 1,
          'encoder_hid_dim': self.text_embedder_hidden_size,
          'encoder_hid_dim_type': 'text_image_proj',
          'cross_attention_dim': self.cross_attention_dim,
          'attention_head_dim': 4,
          'resnet_time_scale_shift': 'scale_shift',
          'class_embed_type': None,
      }

      lowerCamelCase_       	=		UNetaDConditionModel(**__SCREAMING_SNAKE_CASE   )
      return model






    @property
    def   UpperCamelCase			(			self	:				Tuple   )	->						Optional[Any]:
      return {
          "block_out_channels": [32, 64],
          "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
          "in_channels": 3,
          "latent_channels": 4,
          "layers_per_block": 1,
          "norm_num_groups": 8,
          "norm_type": "spatial",
          "num_vq_embeddings": 12,
          "out_channels": 3,
          "up_block_types": [
              "AttnUpDecoderBlock2D",
              "UpDecoderBlock2D",
          ],
          "vq_embed_dim": 4,
      }






    @property
    def   UpperCamelCase			(			self	:				Union[str, Any]   )	->						Optional[Any]:
      torch.manual_seed(0   )
      lowerCamelCase_       	=		VQModel(**self.dummy_movq_kwargs   )
      return model






    def   UpperCamelCase			(			self	:				Union[str, Any]   )	->						List[Any]:
      lowerCamelCase_       	=		self.dummy_text_encoder
      lowerCamelCase_       	=		self.dummy_tokenizer
      lowerCamelCase_       	=		self.dummy_unet
      lowerCamelCase_       	=		self.dummy_movq

      lowerCamelCase_       	=		{
          'num_train_timesteps': 1000,
          'beta_schedule': 'linear',
          'beta_start': 0.00_085,
          'beta_end': 0.012,
          'clip_sample': False,
          'set_alpha_to_one': False,
          'steps_offset': 0,
          'prediction_type': 'epsilon',
          'thresholding': False,
      }

      lowerCamelCase_       	=		DDIMScheduler(**__SCREAMING_SNAKE_CASE   )

      lowerCamelCase_       	=		{
          'text_encoder': text_encoder,
          'tokenizer': tokenizer,
          'unet': unet,
          'scheduler': scheduler,
          'movq': movq,
      }

      return components






    def   UpperCamelCase			(			self	:				Dict			,		__SCREAMING_SNAKE_CASE	:				int			,		__SCREAMING_SNAKE_CASE	:				Dict=0   )	->						str:
      lowerCamelCase_       	=		floats_tensor((1, self.cross_attention_dim)			,		rng=random.Random(__SCREAMING_SNAKE_CASE   )   ).to(__SCREAMING_SNAKE_CASE   )
      lowerCamelCase_       	=		floats_tensor((1, self.cross_attention_dim)			,		rng=random.Random(seed + 1   )   ).to(__SCREAMING_SNAKE_CASE   )
      # create init_image
      lowerCamelCase_       	=		floats_tensor((1, 3, 64, 64)			,		rng=random.Random(__SCREAMING_SNAKE_CASE   )   ).to(__SCREAMING_SNAKE_CASE   )
      lowerCamelCase_       	=		image.cpu().permute(0			,		2			,		3			,		1   )[0]
      lowerCamelCase_       	=		Image.fromarray(np.uinta(__SCREAMING_SNAKE_CASE   )   ).convert('RGB'   ).resize((256, 256)   )

      if str(__SCREAMING_SNAKE_CASE   ).startswith('mps'   ):
        lowerCamelCase_       	=		torch.manual_seed(__SCREAMING_SNAKE_CASE   )
      else:
        lowerCamelCase_       	=		torch.Generator(device=__SCREAMING_SNAKE_CASE   ).manual_seed(__SCREAMING_SNAKE_CASE   )
      lowerCamelCase_       	=		{
          'prompt': 'horse',
          'image': init_image,
          'image_embeds': image_embeds,
          'negative_image_embeds': negative_image_embeds,
          'generator': generator,
          'height': 64,
          'width': 64,
          'num_inference_steps': 10,
          'guidance_scale': 7.0,
          'strength': 0.2,
          'output_type': 'np',
      }
      return inputs






    def   UpperCamelCase			(			self	:				Tuple   )	->						Any:
      lowerCamelCase_       	=		'cpu'

      lowerCamelCase_       	=		self.get_dummy_components()

      lowerCamelCase_       	=		self.pipeline_class(**__SCREAMING_SNAKE_CASE   )
      lowerCamelCase_       	=		pipe.to(__SCREAMING_SNAKE_CASE   )

      pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE   )

      lowerCamelCase_       	=		pipe(**self.get_dummy_inputs(__SCREAMING_SNAKE_CASE   )   )
      lowerCamelCase_       	=		output.images

      lowerCamelCase_       	=		pipe(
          **self.get_dummy_inputs(__SCREAMING_SNAKE_CASE   )			,		return_dict=__SCREAMING_SNAKE_CASE			,		)[0]

      lowerCamelCase_       	=		image[0, -3:, -3:, -1]
      lowerCamelCase_       	=		image_from_tuple[0, -3:, -3:, -1]

      assert image.shape == (1, 64, 64, 3)

      lowerCamelCase_       	=		np.array(
          [0.61_474_943, 0.6_073_539, 0.43_308_544, 0.5_928_269, 0.47_493_595, 0.46_755_973, 0.4_613_838, 0.45_368_797, 0.50_119_233]   )
      assert (
          np.abs(image_slice.flatten() - expected_slice   ).max() < 1e-2
      ), F''' expected_slice {expected_slice}, but got {image_slice.flatten()}'''
      assert (
          np.abs(image_from_tuple_slice.flatten() - expected_slice   ).max() < 1e-2
      ), F''' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'''






@slow
@require_torch_gpu
class 	a		(   unittest.TestCase						):






    def   UpperCamelCase			(			self	:				Tuple   )	->						List[str]:
      # clean up the VRAM after each test
      super().tearDown()
      gc.collect()
      torch.cuda.empty_cache()






    def   UpperCamelCase			(			self	:				Any   )	->						int:
      lowerCamelCase_       	=		load_numpy(
          'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
          '/kandinsky/kandinsky_img2img_frog.npy'   )

      lowerCamelCase_       	=		load_image(
          'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/kandinsky/cat.png'   )
      lowerCamelCase_       	=		'A red cartoon frog, 4k'

      lowerCamelCase_       	=		KandinskyPriorPipeline.from_pretrained(
          'kandinsky-community/kandinsky-2-1-prior'			,		torch_dtype=torch.floataa   )
      pipe_prior.to(__SCREAMING_SNAKE_CASE   )

      lowerCamelCase_       	=		KandinskyImgaImgPipeline.from_pretrained(
          'kandinsky-community/kandinsky-2-1'			,		torch_dtype=torch.floataa   )
      lowerCamelCase_       	=		pipeline.to(__SCREAMING_SNAKE_CASE   )

      pipeline.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE   )

      lowerCamelCase_       	=		torch.Generator(device='cpu'   ).manual_seed(0   )
      lowerCamelCase_	,      lowerCamelCase_       	=		pipe_prior(
          __SCREAMING_SNAKE_CASE			,		generator=__SCREAMING_SNAKE_CASE			,		num_inference_steps=5			,		negative_prompt=''			,		).to_tuple()

      lowerCamelCase_       	=		pipeline(
          __SCREAMING_SNAKE_CASE			,		image=__SCREAMING_SNAKE_CASE			,		image_embeds=__SCREAMING_SNAKE_CASE			,		negative_image_embeds=__SCREAMING_SNAKE_CASE			,		generator=__SCREAMING_SNAKE_CASE			,		num_inference_steps=100			,		height=768			,		width=768			,		strength=0.2			,		output_type='np'			,		)

      lowerCamelCase_       	=		output.images[0]

      assert image.shape == (768, 768, 3)

      assert_mean_pixel_difference(__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE   )




